Project 1 Guidelines.

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Presentation transcript:

Project 1 Guidelines

Parts to discuss Data Sets Test Strategies Explanation of Results Theoretical Complexities Classical vs Divide and Conquer Strength/ Constraints MUST print this paper and place on top of report (found on website)

Data Sets Tables Label corresponding algorithm to color of graph List time unit measured, and what you are recording Include one graph comparing all algorithms together Label corresponding algorithm to color of graph Size matrix(nxn) Alg 1 (ns) Alg 2 Alg 3 2x2 .01 1.2 3.2 4x4 .05 1.5 3.5 8x8 … Each cell in table is average run time (measured in APPROPRIATE time units) for example, on a 2x2 matrix alg 1 was ran 5 times to get an average run time of .01 nano seconds.

Test Strategies Consider the following: How did you test your algorithms? Ran everything in one for loop? Tested each indicidually? etc How was the matrix filled? I.E. did you use a random number generator to fill the whole thing? How many times on average did you run each trial to collect data for your table? Etc….

Explanation of Results What algorithm did you expect to be the fastest? Was your prediction correct? Why or why not? Ex: I expected algorithm one to run faster because ….. But as it turns out algorithm two was faster. This can be contributed to… Algorithm one ran slower due to … Did any algorithm run as expected? Give reasons to explain your data that was collected

Theoretical Complexity Comparison Please analyze your code and give complexity in big o notation. Example: The piece of code that contributes to the most time taken in algorithm one is iterating through a 2d array. As there are two for loops used to iterate through the said array, results in a complexity of O(n^2). NOTE: If there is a recurrence relation please state what it is, why was the relation created in that way, as well as what it reduces down to. If there is recursion, there is a recurrence relation.

Classical vs Divide and Conquer Please briefly re-iterate time complexities Are there situations where one might perform better than the other? Give reasoning Which did you find easier to implement and why

Strengths and Constraints What are some good things in your code? What computer was used? Were there other programs running in the background as you were testing? Could this affect your data? What could you have done better given if you did not have certain constraints?

Hard copy of your code